Are You A Risk Taker? Adversarial Learning of Asymmetric Cross-Domain Alignment for Risk Tolerance Prediction

Abstract

Most current studies on survey analysis and risk tolerance modelling lack professional knowledge and domain-specific models. Given the effectiveness of generative adversarial learning in cross-domain information, we design an Asymmetric cross-Domain Generative Adversarial Network (ADGAN) for domain scale inequality. ADGAN utilizes the information-sufficient domain to provide extra information to improve the representation learning on the information-insufficient domain via domain alignment. We provide data analysis and user model on two data sources: Consumer Consumption Information and Survey Information. We further test ADGAN on a real-world dataset with view embedding structures and show ADGAN can better deal with the class imbalance and unqualified data space than state-of-the-art, demonstrating the effectiveness of leveraging asymmetrical domain information.

References in corpus (8)

• XGBoost: A Scalable Tree Boosting System
• Conditional Generative Adversarial Nets
• Large Scale GAN Training for High Fidelity Natural Image Synthesis
• Wasserstein GAN
• Semi-Supervised Learning with Generative Adversarial Networks
• Adversarial Variational Embedding for Robust Semi-supervised Learning
• Adversarial Domain Adaptation for Stable Brain-Machine Interfaces
• FAIRY: A Framework for Understanding Relationships between Users' Actions and their Social Feeds

Cited by in corpus (1)

• Face to Purchase: Predicting Consumer Choices with Structured Facial and Behavioral Traits Embedding